Data retention policies for AI

Data retention policies for AI define how long different types of data used in artificial intelligence systems should be stored, when it should be deleted and under what conditions it can be archived. These policies apply to raw input data, processed datasets, model outputs and logs or audit trails used during model development and monitoring.

AI systems often rely on large volumes of personal, sensitive or proprietary data. Holding on to this data too long can increase compliance risks, while deleting it too soon may undermine explainability or legal defense. For governance and compliance teams, data retention policies help balance privacy, legal and operational requirements, especially under standards like ISO/IEC 42001 and laws such as the GDPR.

According to the 2023 AI Data Lifecycle Study, nearly 60% of companies using AI lack clear retention rules for data used in model training or inference.

Why AI needs specific retention rules

AI projects present unique retention challenges compared to standard IT systems. Data often flows through multiple environments including collection, preprocessing, training, evaluation and monitoring. Some AI systems require reprocessing of historical data for model retraining or auditing.

AI models trained on personal or regulated data may retain characteristics of that data even after deletion. Without clear retention timelines and disposal mechanisms, companies risk violating data minimization principles and legal limits on processing duration.

Components of a retention policy

A well-crafted retention policy should define several things.

Types of data covered includes raw, labeled, derived, metadata, logs and model outputs. Location of data covers storage systems, cloud environments and backup systems. Retention duration varies by purpose such as training data versus inference logs. Responsibility assigns clear roles for review, enforcement and updates. Deletion methods specify secure deletion approaches and audit trails. Exceptions and overrides address when data must be kept longer for audits or legal claims.

Policies should be reviewed regularly and adapted to new AI use cases or regulatory changes.

How retention policies work in practice

A digital health platform trained AI models using patient data under consent-based agreements. Its retention policy required all personally identifiable information to be deleted 12 months after collection unless explicitly extended by the patient. When regulators audited the platform under HIPAA, the detailed logs and deletion records helped the company pass with no violations.

In another case, an online service provider failed to delete log data that influenced an AI recommendation engine. An investigation found that this data had been used beyond the stated retention period, resulting in a €2.5 million fine under the GDPR.

Implementing retention policies effectively

Retention policies work best when integrated into the AI pipeline and automated wherever possible.

Mapping the data lifecycle shows how and where data is used throughout the AI system. Tagging data with retention metadata includes expiry dates or classification labels that trigger alerts. Automating deletion processes uses tools that support secure and verified deletion of datasets. Logging retention events maintains audit trails to prove compliance. Involving legal and compliance teams ensures policies reflect external regulations and internal governance standards. Testing policies simulates expiration scenarios and validates that deletion workflows work as expected.

Platforms like BigID, Collibra and open frameworks like Apache Ranger help manage policies across data lakes.

FAQ

Does the GDPR set a specific retention period?

The GDPR requires data to be stored no longer than necessary but does not define exact durations. Companies must assess necessity and document their decisions.

Should AI training data be kept forever?

Retention should match the model's lifecycle and comply with data protection rules. Pseudonymization or synthetic data can extend usability in some cases.

What about model outputs and logs?

These are often overlooked but can contain sensitive information. They should be covered under retention policies and stored with the same care as training data.

Who approves the retention schedule?

A cross-functional group typically includes data stewards, legal, compliance and IT security teams. Final authority may rest with a data governance council or privacy officer.

How long should AI training data be retained?

Retention depends on: legal requirements (EU AI Act specifies 10 years for high-risk systems), audit needs, retraining requirements, and storage costs. Balance retention value against privacy obligations and breach risk. Some regulations require ability to demonstrate compliance, which may require retaining evidence. Document retention decisions and rationale.

How do you handle deletion requests for data used in AI training?

Deletion requests create challenges when data influenced model training. Options include: retraining without the data, demonstrating the data has minimal model impact, or acknowledging inability to fully remove influence. Document processes for handling requests. Consider this challenge when designing data collection and training pipelines.

Should model artifacts be subject to retention policies?

Yes. Model artifacts (weights, parameters, logs) should have defined retention periods aligned with data retention. Older model versions may be needed for audit or incident investigation. Balance retention with storage costs and security risks of maintaining old artifacts. Include model artifacts in data governance programs.

Summary

Data retention policies for AI help manage risk, protect privacy and ensure accountability throughout the AI lifecycle. Without clear rules, data can accumulate beyond legal or ethical limits. Building structured and enforceable policies aligned with ISO/IEC 42001 helps teams create AI systems that are responsible and compliant.